State Machine and Downhill Simplex Approach for Vision‐Based Nighttime Vehicle Detection |
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| Authors: | Kyoung‐Ho Choi Do‐Hyun Kim Kwang‐Sup Kim Jang‐Woo Kwon Sang‐Il Lee Ken Chen Jong‐Hyun Park |
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| Affiliation: | 1. Kyoung‐Ho Choi (phone: +82 10 3034 5803, khchoi@mokpo.ac.kr) and Sang‐Il Lee (leesi@mokpo.ac.kr) are with the Department of Electronics Engineering, Mokpo National University, Mokpo, Rep. of Korea.;2. Do‐Hyun Kim (dohyun@etri.re.kr) and Jong‐Hyun Park (jhp@etri.re.kr) are with the IT Convergence Technology Research Laboratory, ETRI, Daejeon, Rep. of Korea.;3. Kwang‐Sup Kim (kskim@hunsol.com) is the CEO of HUNS Inc., Korea.;4. Jang‐Woo Kwon (jwkwon@inha.ac.kr) is with the School of Computer Engineering and Information, Inha University, Incheon, Rep. of Korea.;5. Ken Chen (chenken@nbu.edu.cn) is with the Department of Information Science and Engineering, Ningbo University, Zhejiang, China. |
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| Abstract: | In this paper, a novel vision‐based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection. |
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| Keywords: | Nighttime vehicle detection state machine downhill simplex headlight detection |
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